code: def plot_results(arrays_dict, gap_data):
      """
      Plot predictions of models
  
      :param arrays_dict: dictionary with 4 keys ('ridge', 'local_poly',
      'batch_poly', 'linear') that can be used to get arrays without gaps
      :param gap_data: an array with gaps
      """
  
      gap_ids = np.ravel(np.argwhere(gap_data == -100.0))
      masked_array = np.ma.masked_where(gap_data == -100.0, gap_data)
  
      plt.plot(
          arrays_dict.get("local_poly"),
          c="orange",
          alpha=0.5,
          label="Local polynomial approximation",
      )
      plt.plot(
          arrays_dict.get("batch_poly"),
          c="green",
          alpha=0.5,
          label="Batch polynomial approximation",
      )
      plt.plot(
          arrays_dict.get("linear"),
          c="black",
          alpha=0.5,
          label="Linear interpolation",
      )
      plt.plot(
          arrays_dict.get("ridge"),
          c="red",
          alpha=0.5,
          label="Inverse ridge",
      )
      plt.plot(masked_array, c="blue", alpha=1.0, label="Actual values")
      plt.xlim(gap_ids[0] - 10, gap_ids[-1] + 10)
      plt.legend(loc="upper center")
      plt.grid()
      plt.show()
  
file_path: FEDOT/examples/simple/time_series_forecasting/gapfilling.py
imports: import matplotlib.pyplot
  import numpy
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.node import PipelineNode
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
  from fedot.utilities.ts_gapfilling import ModelGapFiller, SimpleGapFiller
label_desc: Use image node_id 4 for calling a global function with example usage: plot_results(arrays_dict, gap_data) without return types